CN116127201A - Large-scale user recommendation method based on evolutionary multitasking - Google Patents

Large-scale user recommendation method based on evolutionary multitasking Download PDF

Info

Publication number
CN116127201A
CN116127201A CN202310167737.1A CN202310167737A CN116127201A CN 116127201 A CN116127201 A CN 116127201A CN 202310167737 A CN202310167737 A CN 202310167737A CN 116127201 A CN116127201 A CN 116127201A
Authority
CN
China
Prior art keywords
user
generation
recommended
recommendation
article
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202310167737.1A
Other languages
Chinese (zh)
Inventor
马海平
胡以葳
田野
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Anhui University
Original Assignee
Anhui University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Anhui University filed Critical Anhui University
Priority to CN202310167737.1A priority Critical patent/CN116127201A/en
Publication of CN116127201A publication Critical patent/CN116127201A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9536—Search customisation based on social or collaborative filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Databases & Information Systems (AREA)
  • Biophysics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Evolutionary Biology (AREA)
  • General Physics & Mathematics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Biomedical Technology (AREA)
  • Genetics & Genomics (AREA)
  • Artificial Intelligence (AREA)
  • Physiology (AREA)
  • Computational Linguistics (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The invention discloses a large-scale user recommendation method based on evolutionary multitasking, which comprises the following steps: step 1, acquiring a user and article interaction data set, and step 2, excavating preference scores of the user on articles by constructing a neural network algorithm to obtain a scoring matrix of the user on the articles; step 3: grouping users through clustering, and taking users with similar preference interests as the same category; step 4, initializing and generating a multi-task population; and 5, performing information migration among individuals in the same user group, performing information migration among populations among different user groups, iteratively selecting an optimal user solution through environment selection, and finally selecting an optimal solution as a recommendation list of the user. The invention can reduce the time and space consumed in large-scale user recommendation optimization problem, and improves the accuracy of predicting the user recommendation result through the clustering technology.

Description

Large-scale user recommendation method based on evolutionary multitasking
Technical Field
The invention belongs to the crossing field of evolutionary computation and data mining, and particularly relates to a recommendation method based on evolutionary multitasking optimization.
Background
The aim of the recommendation system is to help users to screen useful information from massive data, the traditional recommendation system only considers the recommendation accuracy, and besides the accuracy, other performances such as novelty, diversity and the like are also important indexes of the recommendation system, so that the multi-target recommendation system becomes an important research direction of the recommendation system. However, as the recommendation system needs to meet multiple indexes, optimizing some indexes inevitably brings conflicts to some indexes. Therefore, multi-objective optimization becomes an important technical means for solving the recommendation system.
The existing multi-objective optimization method of the recommendation system comprises the following steps: the multi-objective index is weighted and summed into a scalar method of a single objective problem, or a population-based evolutionary algorithm is used to simultaneously optimize multiple objectives.
Scalar methods first typically sum two target weights and then combine the scalar with pareto high efficiency SGD using a multiple gradient descent algorithm, using KKT conditions to direct the updating of scalar weights. However, this method can only be optimized for targets with gradients.
When the evolutionary algorithm optimizes the multi-objective recommendation system, the evolutionary algorithm is usually operated for each user respectively, however, the evolutionary algorithm is used as an iterative algorithm, and when the number of users in the data set is excessive, the algorithm is sequentially optimized, so that the operation time of the algorithm is excessively long. Or the recommended results of the users are borrowed into one chromosome through real solution, and all users are simultaneously optimized in one optimization process, but the method can lead to overlong encoding length of the chromosome, and is difficult to simultaneously optimize all users to be optimal, so that the optimization method is easy to fall into a local optimal solution, and the recommended article results can not meet the user requirements.
Disclosure of Invention
The invention provides a large-scale user recommending method based on evolutionary multitasking for solving the defects in the prior art, thereby realizing a recommending list containing accuracy, novelty and diversity for users and simultaneously ensuring the high efficiency and the sum of the recommending methods.
The invention adopts the following technical scheme for solving the technical problems:
the invention discloses a large-scale user recommendation method based on evolutionary multitasking, which is characterized by comprising the following steps:
step one, acquiring related data of a user and an article:
acquiring a user set s= { S 1 ,s 2 ,…,s u ,…,s |S| -wherein S represents the number of users S u The user u is represented;
acquiring an item set q= { Q 1 ,q 2 ,…,q i ,…,q |Q| -wherein Q represents the number of items, Q i Representing an ith item;
acquiring a user article interaction data set, taking interaction data of each user and articles as positive samples, and randomly collecting articles which are not interacted by the user as negative samples;
step two, obtaining a scoring matrix of the user on the article through a prediction model, wherein the prediction model comprises the following steps: the system comprises a coding layer, a full connection layer, an attention interaction layer, an interaction output layer and a prediction layer;
step 2.1, the coding layer is used for the u number user s u And the ith article q i Performing one-hot coding to obtain u-number user s u Is a sparse vector theta of (2) u And article q i Is a sparse vector of (2)
Figure BDA0004096640000000026
Then mapping the two sparse vectors to E-dimensional vectors respectively, and obtaining u-number user s through mapping of the full connection layer u Is a representation vector p of (2) u And the ith article q i Is a representation vector o of (2) i ；
Step 2.2, the attention interaction layer pair p u And o i Processing and outputting attention vector a u,i ；
Step 2.3, the interactive output layer will p u And o i After splicing, the vector is combined with the attention vector a u,i After dot multiplication, obtaining a u-number user s u And the ith article q i Is of the interaction vector f u,i ；
Step 2.4, the prediction layer pair interaction vector f u,i After multi-layer full-connection processing, a predicted interaction score r is output u,i ；
Step 2.5, taking the mean square error of the minimized predicted interaction score as a loss function, and optimizing the prediction model by using an Adam algorithm until the maximum iteration number is reached, so as to obtain an optimal prediction model and an interaction score matrix of each item predicted by each user output by the optimal prediction model;
step three, setting the clustering number as K, wherein top represents the number of candidate articles, and selecting u-number users s u Top preferred items of (a) as candidate item set candidates u Counting the number of the same articles in the candidate article sets of each user as the similarity between the corresponding users; users with similar similarity are used as a class through a clustering algorithm, so that the users are divided into K groups by S|, and a user set U= { U is obtained 1 ,U 2 ,…,U j ,…,U K }，U j User group representing a j-th group category, and U j ＝{P j,1 ,P j,2 ,…,P j,m ,…,P j,M }；P j,m Representing U j P-th user of (a);
step four, initializing a population:
step 4.1, defining the current iteration number as L, the maximum iteration number as L, enabling N to be the population individual number, and adopting a real number system to enable the user group U of the j group category to be the j group j N recommendation results of each user are respectively encoded into an individual with the length of T, and each decision variable of the individual represents the serial number of the recommended article; thus, N recommendation results of one user form a group, and the mth user P is led to j,m N recommended results of (a) are recorded as the mth species of the first generationGroup, user group U of j-th group category j All first generation populations in (a) are marked as
Figure BDA0004096640000000021
U is set to j M th user P j,m The nth recommendation of (2) is recorded as the nth individual in the mth population of the first generation
Figure BDA0004096640000000022
And is also provided with
Figure BDA0004096640000000023
Figure BDA0004096640000000024
Representing the mth generation of users P j,m The number of the t recommended article in the nth recommended result;
step 4.2, from the mth user P, based on the interaction score matrix predicted by each user for each item j,m Candidate item set of (5) j,m Randomly selecting the serial numbers of T non-repeated articles for pairing
Figure BDA0004096640000000025
Initializing;
step 4.3, obtaining the mth generation of the mth user P by using the formula (1) j,m Is the nth recommendation result of (2)
Figure BDA0004096640000000031
Accuracy index>
Figure BDA0004096640000000032
Figure BDA0004096640000000033
In the formula (1), the components are as follows,
Figure BDA0004096640000000034
representing the mth user P j,m For serial number->
Figure BDA0004096640000000035
Scoring the corresponding item;
obtaining the mth generation user P by using the method (2) j,m Is the nth recommendation result of (2)
Figure BDA0004096640000000036
Novel rate index of (a)
Figure BDA0004096640000000037
/>
Figure BDA0004096640000000038
In formula (2), a polar t Indicating serial number
Figure BDA0004096640000000039
The popularity of the corresponding item;
obtaining the mth generation user P by using the method (3) j,m Is the nth recommendation result of (2)
Figure BDA00040966400000000310
Is a diversity index of (2)
Figure BDA00040966400000000311
Figure BDA00040966400000000312
In the formula (3), label
Figure BDA00040966400000000313
Indicating number->
Figure BDA00040966400000000314
Category label of corresponding article, label all Representing user item interaction data set propertiesCategory labels of the products;
constructing a first generation multi-objective optimization function maxisize by using the formula (4)
Figure BDA00040966400000000315
Figure BDA00040966400000000316
And 5, performing information migration among individuals among the same user group, performing information migration among populations among different user groups, and iteratively selecting an optimal user solution through environment selection.
The large-scale user recommendation method based on evolutionary multitasking of the invention is also characterized in that the step 5 comprises:
step 5.1, information migration among individuals is carried out among the same user group:
step 5.1.1 Using binary tournament selection method based on equation (4)
Figure BDA00040966400000000317
Selecting 2 XN recommended results to participate in evolution to obtain first generation mating pool ∈>
Figure BDA00040966400000000318
Step 5.1.2 from the first Generation pool
Figure BDA00040966400000000319
Selecting two recommended results of the first generation and marking the recommended results as +.>
Figure BDA00040966400000000320
Figure BDA00040966400000000321
And->
Figure BDA00040966400000000322
And performing cross operation to obtain two first generationCross recommendation result->
Figure BDA00040966400000000323
Wherein (1)>
Figure BDA00040966400000000324
Representation->
Figure BDA00040966400000000325
The number of the t-th recommended item,/-)>
Figure BDA00040966400000000326
Representation->
Figure BDA00040966400000000327
The number of the t recommended article;
step 5.1.3 with probability m P For a pair of
Figure BDA00040966400000000328
Performing mutation operation:
randomly selecting a number r' from {1,2,3 …, T }, from
Figure BDA0004096640000000041
Candidate item sets corresponding to affiliated users j,1 Is selected randomly from one and->
Figure BDA0004096640000000042
The order of the r' th recommended item +.>
Figure BDA0004096640000000043
The different article serial numbers are replaced, thereby obtaining +.>
Figure BDA0004096640000000044
Recommended first generation variant of ++>
Figure BDA0004096640000000045
Step 5.1.4 for the first Generation pool
Figure BDA0004096640000000046
After crossing and mutation operations are carried out on all recommended results in the first generation of all populations according to the steps 5.1.2-5.1.3, mutation recommended results of all populations in the first generation are obtained>
Figure BDA0004096640000000047
Will->
Figure BDA0004096640000000048
And->
Figure BDA0004096640000000049
User group U combined into j-th group category j The method comprises the steps of (1) measuring the fitness value of each recommended result in the first-generation M new populations through a formula (4), so that environment selection is carried out on the first-generation M new populations through non-dominant sorting and crowding distances, and the optimal N recommended results are reserved as the first-generation (1) M populations;
step 5.2, information migration among the populations is carried out among different user groups:
if the mth generation of users P j,m If more than half of the recommended results are unchanged for a plurality of successive generations, computing a user group U of the j-th group category j Similarity between user groups of other group categories, and selecting the user group with highest similarity for the mth user P of the first generation j,m Performing crossover and mutation operations between all recommended results of (1) so as to obtain an mth population of the (1+1) th generation according to the process of the step 5.1.4;
step 5.3, after assigning l+1 to L, judging whether L reaches L, if not, returning to step 4.3 for sequential execution, otherwise, selecting an individual from the mth population of the L generation as a user group U of the j-th group class j M th user P j,m Is a recommended result of the user.
The step 5.1.2 comprises:
step a, judging
Figure BDA00040966400000000410
And->
Figure BDA00040966400000000411
If the users belong to the same user, executing the step b, otherwise, executing the step c;
step b, pairing
Figure BDA00040966400000000412
And->
Figure BDA00040966400000000413
With probability c P Performing crossover operation:
randomly selecting a number r from {1,2,3 …, T }, will
Figure BDA00040966400000000414
The former r position and->
Figure BDA00040966400000000415
The first r bits of (a) are exchanged to obtain the first generation two cross recommended results +.>
Figure BDA00040966400000000416
And->
Figure BDA00040966400000000417
Figure BDA00040966400000000418
Wherein (1)>
Figure BDA00040966400000000419
Representation->
Figure BDA00040966400000000420
The order of the r-th recommended item,/-)>
Figure BDA00040966400000000421
Representation of
Figure BDA00040966400000000422
Middle (f)The serial numbers of r recommended articles;
step c, will
Figure BDA00040966400000000423
And->
Figure BDA00040966400000000424
Combining to obtain a first-generation new recommended result, using formula (1) as fitness value, using binary competitive competition method to respectively select serial numbers corresponding to T items from the first-generation new recommended result and respectively forming two first-generation cross recommended results->
Figure BDA00040966400000000425
To->
Figure BDA00040966400000000426
As->
Figure BDA00040966400000000427
Cross-recommendation results generated by +.>
Figure BDA00040966400000000428
As->
Figure BDA00040966400000000429
The cross recommendation results are generated.
The electronic device of the invention comprises a memory and a processor, wherein the memory is used for storing a program for supporting the processor to execute any large-scale user recommendation method, and the processor is configured to execute the program stored in the memory.
The invention provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program is executed by a processor to execute the steps of any large-scale user recommendation method.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, the evolutionary multitasking is used for optimizing the multi-target recommendation system for the first time, the traditional optimization method can only generate an optimized recommendation result for one user at a time, and correlation among different tasks is not considered. According to the method and the device, the optimal recommendation results of a plurality of users can be simultaneously generated in one optimization process, so that the calculated amount of a recommendation algorithm is greatly reduced.
2. In order to avoid the negative migration phenomenon possibly caused by simultaneous optimization, the invention uses a clustering algorithm to take a plurality of users with similar interests in data as a user group in an optimization method, and designs a new operator to accelerate the convergence of the algorithm, thereby improving the recommendation speed.
3. According to the invention, by designing a population optimization scheme, genetic operators and information migration strategies of different populations, the calculated amount is reduced, the recommendation efficiency is improved, and the recommendation accuracy is ensured.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a diagram illustrating an example of user clustering in accordance with the present invention;
FIG. 3 is an exemplary diagram of population initialization in accordance with the present invention;
FIG. 4 is a diagram illustrating the generation of a next generation population according to the present invention.
Detailed Description
In this embodiment, a large-scale user recommendation method based on evolutionary multitasking, as shown in fig. 1, is performed according to the following steps:
step one, acquiring related data of a user and an article:
acquiring a user set s= { S 1 ,s 2 ,…,s u ,…,s |S| -wherein S represents the number of users S u The user u is represented;
acquiring an item set q= { Q 1 ,q 2 ,…,q i ,…,q |Q| -wherein Q represents the number of items, Q i Representing an ith item;
acquiring a user article interaction data set, taking interaction data of each user and articles as positive samples, and randomly collecting articles which are not interacted by the user as negative samples;
step two, obtaining a scoring matrix of the user on the article through a prediction model, wherein the prediction model comprises: the system comprises a coding layer, a full connection layer, an attention interaction layer, an interaction output layer and a prediction layer;
step 2.1, coding layer pair u number user s u And the ith article q i Performing one-hot coding to obtain u-number user s u Is a sparse vector theta of (2) u And article q i Is a sparse vector of (2)
Figure BDA0004096640000000051
Then mapping the two sparse vectors to E-dimensional vectors respectively, and obtaining u-number user s through mapping of a full connection layer u Is a representation vector p of (2) u And the ith article q i Is a representation vector o of (2) i ；
Step 2.2, attention interaction layer pair p u And o i Processing and outputting attention vector a u,i ；
Step 2.3, the interaction output layer outputs p u And o i After splicing, the vector is combined with the attention vector a u,i After dot multiplication, obtaining a u-number user s u And the ith article q i Is of the interaction vector f u,i ；
Step 2.4, prediction layer pair interaction vector f u,i After multi-layer full-connection processing, a predicted interaction score r is output u,i ；
Step 2.5, taking the mean square error of the interaction score of the minimum prediction as a loss function, and optimizing the prediction model by using an Adam algorithm until the maximum iteration number is reached, so as to obtain an optimal prediction model and an interaction score matrix of each user output by the optimal prediction model for each article;
step three, setting the clustering number as K, wherein top represents the number of candidate articles, and selecting u-number users s u Top preferred items of (a) as candidate item set candidates u Counting the number of the same articles in the candidate article sets of each user as the similarity between the corresponding users; users with similar similarity are used as a class through a clustering algorithm, so that the users are classified into K groups by S, and in the embodiment, the method comprises the following steps ofK= |s|/10 and a user set u= { U is obtained 1 ,U 2 ,…,U j ,…,U K }，U j User group representing a j-th group category, and U j ＝{P j,1 ,P j,2 ,…,P j,m ,…,P j,M }；P j,m Representing U j P-th user of (a); as in the example of fig. 2, it is assumed that there are 4 users s 1 ,s 2 ,s 3 ,s 4 The candidate item set of (2) is obtained by counting the same item number of different users 1 Sum s 2 Is 7,s 3 Sum s 4 The similarity of (2) is 7, so the final clustering result is s 1 Sum s 2 Is U (U) 1 Class, s 3 Sum s 4 Is U (U) 2 Class.
Step four, initializing a population:
step 4.1, defining the current iteration number as L and the maximum iteration number as L, in this embodiment, setting l=100, and making N be the population individual number, in this embodiment, setting n=10, and using real number system to make user group U of the j-th group category j N recommendation results of each user are respectively encoded into an individual with a length of T, in this embodiment, t=10 is set, and each decision variable of the individual represents a serial number of a recommended article; thus, N recommendation results of one user form a group, and the mth user P is led to j,m The N recommended results of the (a) are recorded as the mth population of the first generation, and the user group U of the jth group category j All first generation populations in (a) are marked as
Figure BDA0004096640000000061
U is set to j M th user P j,m The nth recommendation of (2) is recorded as the nth individual in the mth population of the first generation
Figure BDA0004096640000000062
And is also provided with
Figure BDA0004096640000000063
Figure BDA0004096640000000064
Representing the mth generation of users P j,m The nth recommended article serial number in the nth recommended result, the population individuals are composed of the recommended results of all users of the group, and the recommended results are encoded into a matrix. Fig. 3 shows an example of a class of population individuals with a number of users of 2 and a recommended length of 10.
Step 4.2, from the mth user P, based on the interaction score matrix predicted by each user for each item j,m Candidate item set of (5) j,m Randomly selecting the serial numbers of T non-repeated articles for pairing
Figure BDA0004096640000000065
Initializing;
step 4.3, obtaining the mth generation of the mth user P by using the formula (1) j,m Is the nth recommendation result of (2)
Figure BDA0004096640000000066
Accuracy index>
Figure BDA0004096640000000067
Figure BDA0004096640000000068
In the formula (1), the components are as follows,
Figure BDA0004096640000000071
representing the mth user P j,m For->
Figure BDA0004096640000000072
Scoring of the item;
obtaining the mth generation user P by using the method (2) j,m Is the nth recommendation result of (2)
Figure BDA0004096640000000073
Novel rate index of (a)
Figure BDA0004096640000000074
Figure BDA0004096640000000075
In formula (2), a polar t Representation of
Figure BDA0004096640000000076
Popularity of the item;
obtaining the mth generation user P by using the method (3) j,m Is the nth recommendation result of (2)
Figure BDA0004096640000000077
Is a diversity index of (2)
Figure BDA0004096640000000078
Figure BDA0004096640000000079
In the formula (3), label
Figure BDA00040966400000000710
Representation->
Figure BDA00040966400000000711
Category label of article, label all Category labels representing all items in the user item interaction dataset;
constructing a first generation multi-objective optimization function maxisize by using the formula (4)
Figure BDA00040966400000000712
Figure BDA00040966400000000713
Step 5, information migration among individuals is carried out among the same user group, information migration among populations is carried out among different user groups, and an optimal user solution is selected through environment selection iteration;
step 5.1, information migration among individuals is carried out among the same user group:
step 5.1.1 Using binary tournament selection method based on equation (4)
Figure BDA00040966400000000714
Selecting 2 XN recommended results to participate in evolution to obtain first generation mating pool ∈>
Figure BDA00040966400000000715
Step 5.1.2 from the first Generation pool
Figure BDA00040966400000000716
Selecting two recommended results of the first generation and marking the recommended results as +.>
Figure BDA00040966400000000717
Figure BDA00040966400000000718
And->
Figure BDA00040966400000000719
And performing a crossover operation wherein->
Figure BDA00040966400000000720
Representation->
Figure BDA00040966400000000721
The number of the t-th recommended item,/-)>
Figure BDA00040966400000000722
Representation->
Figure BDA00040966400000000723
The number of the t recommended item:
step a, judging
Figure BDA00040966400000000724
And->
Figure BDA00040966400000000725
If the users belong to the same user, executing the step b, otherwise, executing the step c;
step b, pairing
Figure BDA00040966400000000726
And->
Figure BDA00040966400000000727
With probability c P Performing crossover operation:
randomly selecting a number r from {1,2,3 …, T }, will
Figure BDA00040966400000000728
The former r position and->
Figure BDA00040966400000000729
The first r bits of (a) are exchanged to obtain the first generation two cross recommended results +.>
Figure BDA00040966400000000730
And->
Figure BDA00040966400000000731
Figure BDA00040966400000000732
Wherein (1)>
Figure BDA00040966400000000733
Representation->
Figure BDA00040966400000000734
The order of the r-th recommended item,/-)>
Figure BDA00040966400000000735
Representation of
Figure BDA00040966400000000736
The number of the r-th recommended article;
step c, will
Figure BDA00040966400000000737
And->
Figure BDA00040966400000000738
Combining to obtain a first-generation new recommended result, using formula (1) as fitness value, using binary competitive competition method to respectively select serial numbers corresponding to T items from the first-generation new recommended result and respectively forming two first-generation cross recommended results->
Figure BDA0004096640000000081
To->
Figure BDA0004096640000000082
As->
Figure BDA0004096640000000083
Cross-recommendation results generated by +.>
Figure BDA0004096640000000084
As->
Figure BDA0004096640000000085
Generating a cross recommendation result;
step 5.1.3 with probability m P For a pair of
Figure BDA0004096640000000086
Performing mutation operation:
randomly selecting a number r' from {1,2,3 …, T }, from
Figure BDA0004096640000000087
Candidate item sets corresponding to affiliated users j,1 Is selected randomly from one and->
Figure BDA0004096640000000088
The r' th recommended article in the list/>
Figure BDA0004096640000000089
The different article serial numbers are replaced, thereby obtaining +.>
Figure BDA00040966400000000810
Recommended first generation variant of ++>
Figure BDA00040966400000000811
Fig. 4 shows an example illustrating the specific operation of the crossover and mutation operator. The class of user population in the example includes 2 users, denoted s 1 ,s 2 ，X 1 ,X 2 Is s 1 ,s 2 Is 10, probability c P ,m P 0.5 and 0.5, respectively. First, the crossover operation is performed, and assuming that the generated random number is 0.3, then for X 1 ,X 2 Cross operation is performed on X 1 Randomly selecting a number from {1,2,..10 }, and if 3, crossing (1,23,15,9,5,12,4,18,22,14), (18,2,16,8,20,24,4,25,17,30) to obtain (1,23,15,8,20,24,4,25,17,30), (18,2,16,9,5,12,4,18,22,14). Let the same assumption X 2 Randomly selecting one number, which is 4, (10,2,7,6,5,3,11,13,17,19), (10,2,15,6,21,12,11,27,28,29) to obtain (10,2,7,6,21,12,11,27,28,29), (10,2,15,6,5,3,11,13,17,19) after crossing, to obtain two children
Figure BDA00040966400000000812
Then, a mutation operator is performed for each child. For->
Figure BDA00040966400000000813
Assuming that the corresponding generated random number is 0.4, then for +.>
Figure BDA00040966400000000814
A mutation operation was performed, and a number selected from {1,2,..10 } was randomly selected, assuming 4. From user s 1 Candidate item set candidates of (c) s1 A new article 6 is selected to replace the original article 8. Pair s of the same theory 2 Performing similar operations, finally->
Figure BDA00040966400000000815
Obtaining->
Figure BDA00040966400000000816
They are two new recommendations.
Step 5.1.4 for the first Generation pool
Figure BDA00040966400000000817
After crossing and mutation operations are carried out on all recommended results in the first generation of all populations according to the steps 5.1.2-5.1.3, mutation recommended results of all populations in the first generation are obtained>
Figure BDA00040966400000000818
Will->
Figure BDA00040966400000000819
And->
Figure BDA00040966400000000820
User group U combined into j-th group category j The method comprises the steps of (1) measuring the fitness value of each recommended result in the first-generation M new populations through a formula (4), so that environment selection is carried out on the first-generation M new populations through non-dominant sorting and crowding distances, and the optimal N recommended results are reserved as the first-generation (1) M populations;
step 5.2, information migration among the populations is carried out among different user groups:
if the mth generation of users P j,m If more than half of the recommended results are unchanged for a plurality of successive generations, computing a user group U of the j-th group category j Similarity between user groups of other group categories, and selecting the user group with highest similarity for the mth user P of the first generation j,m Performing crossover and mutation operations between all recommended results of (1) so as to obtain an mth population of the (1+1) th generation according to the process of the step 5.1.4;
step 5.3, after assigning l+1 to L, judging whether L reaches L, if not, returning to step 4.3 for sequential execution, otherwise, selecting an individual from the mth population of the L generation as a user group U of the j-th group class j M th user P j,m Is a recommended result of the user.
In this embodiment, an electronic device includes a memory for storing a program supporting the processor to execute the above method, and a processor configured to execute the program stored in the memory.
In this embodiment, a computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method described above.

Claims (5)

1. The large-scale user recommendation method based on the evolutionary multitasking is characterized by comprising the following steps of:
step one, acquiring related data of a user and an article:
acquiring a user set s= { S 1 ,s 2 ,…,s u ,…,s |S| -wherein S represents the number of users S u The user u is represented;
acquiring an item set q= { Q 1 ,q 2 ,…,q i ,…,q |Q| -wherein Q represents the number of items, Q i Representing an ith item;
acquiring a user article interaction data set, taking interaction data of each user and articles as positive samples, and randomly collecting articles which are not interacted by the user as negative samples;
step two, obtaining a scoring matrix of the user on the article through a prediction model, wherein the prediction model comprises the following steps: the system comprises a coding layer, a full connection layer, an attention interaction layer, an interaction output layer and a prediction layer;
step 2.1, the coding layer is used for the u number user s u And the ith article q i Performing one-hot coding to obtain u-number user s u Is a sparse vector theta of (2) u And article q i Is sparse toMeasuring amount
Figure QLYQS_1
Then mapping the two sparse vectors to E-dimensional vectors respectively, and obtaining u-number user s through mapping of the full connection layer u Is a representation vector p of (2) u And the ith article q i Is a representation vector o of (2) i ；
Step 2.2, the attention interaction layer pair p u And o i Processing and outputting attention vector a u,i ；
Step 2.3, the interactive output layer will p u And o i After splicing, the vector is combined with the attention vector a u,i After dot multiplication, obtaining a u-number user s u And the ith article q i Is of the interaction vector f u,i ；
Step 2.4, the prediction layer pair interaction vector f u,i After multi-layer full-connection processing, a predicted interaction score r is output u,i ；
Step 2.5, taking the mean square error of the minimized predicted interaction score as a loss function, and optimizing the prediction model by using an Adam algorithm until the maximum iteration number is reached, so as to obtain an optimal prediction model and an interaction score matrix of each item predicted by each user output by the optimal prediction model;
step three, setting the clustering number as K, wherein top represents the number of candidate articles, and selecting u-number users s u Top preferred items of (a) as candidate item set candidates u Counting the number of the same articles in the candidate article sets of each user as the similarity between the corresponding users; users with similar similarity are used as a class through a clustering algorithm, so that the users are divided into K groups by S|, and a user set U= { U is obtained 1 ,U 2 ,…,U j ,…,U K }，U j User group representing a j-th group category, and U j ＝{P j,1 ,P j,2 ,…,P j,m ,…,P j,M }；P j,m Representing U j P-th user of (a);
step four, initializing a population:
step 4.1,Defining the current iteration number as L, the maximum iteration number as L, enabling N to be the population individual number, and adopting a real number system to set the user group U of the j group category j N recommendation results of each user are respectively encoded into an individual with the length of T, and each decision variable of the individual represents the serial number of the recommended article; thus, N recommendation results of one user form a group, and the mth user P is led to j,m The N recommended results of the (a) are recorded as the mth population of the first generation, and the user group U of the jth group category j All first generation populations in (a) are marked as
Figure QLYQS_2
U is set to j M th user P j,m The nth recommendation of (2) is recorded as the nth individual in the mth population of the first generation
Figure QLYQS_3
And is also provided with
Figure QLYQS_4
Figure QLYQS_5
Representing the mth generation of users P j,m The number of the t recommended article in the nth recommended result;
step 4.2, from the mth user P, based on the interaction score matrix predicted by each user for each item j,m Candidate item set of (5) j,m Randomly selecting the serial numbers of T non-repeated articles for pairing
Figure QLYQS_6
Initializing;
step 4.3, obtaining the mth generation of the mth user P by using the formula (1) j,m Is the nth recommendation result of (2)
Figure QLYQS_7
Accuracy index>
Figure QLYQS_8
Figure QLYQS_9
In the formula (1), the components are as follows,
Figure QLYQS_10
representing the mth user P j,m For serial number->
Figure QLYQS_11
Scoring the corresponding item;
obtaining the mth generation user P by using the method (2) j,m Is the nth recommendation result of (2)
Figure QLYQS_12
Novel index->
Figure QLYQS_13
Figure QLYQS_14
In formula (2), a polar t Indicating serial number
Figure QLYQS_15
The popularity of the corresponding item;
obtaining the mth generation user P by using the method (3) j,m Is the nth recommendation result of (2)
Figure QLYQS_16
Diversity index->
Figure QLYQS_17
Figure QLYQS_18
In the formula (3), the amino acid sequence of the compound,
Figure QLYQS_19
indicating the serial number x j l ,m,n,t Category label of corresponding article, label all Category labels representing all items in the user item interaction dataset;
constructing a first generation multi-objective optimization function by using the method (4)
Figure QLYQS_20
Figure QLYQS_21
And 5, performing information migration among individuals among the same user group, performing information migration among populations among different user groups, and iteratively selecting an optimal user solution through environment selection.
2. The evolutionarily multitasking-based large-scale user recommendation method of claim 1, wherein said step 5 comprises:
step 5.1, information migration among individuals is carried out among the same user group:
step 5.1.1 Using binary tournament selection method based on equation (4)
Figure QLYQS_22
Selecting 2 XN recommended results to participate in evolution to obtain first generation mating pool ∈>
Figure QLYQS_23
Step 5.1.2 from the first Generation pool
Figure QLYQS_25
Selecting two recommended results of the first generation and marking the recommended results as +.>
Figure QLYQS_28
Figure QLYQS_30
And->
Figure QLYQS_26
And performing cross operation to obtain two first generation cross recommendation results +.>
Figure QLYQS_29
Wherein (1)>
Figure QLYQS_31
Representation->
Figure QLYQS_32
The number of the t-th recommended item,/-)>
Figure QLYQS_24
Representation->
Figure QLYQS_27
The number of the t recommended article;
step 5.1.3 with probability m P For a pair of
Figure QLYQS_33
Performing mutation operation:
randomly selecting a number r from {1,2,3 …, T } ′ From the slave
Figure QLYQS_34
Candidate item sets corresponding to affiliated users j,1 Is selected randomly from one and->
Figure QLYQS_35
Middle (r) ′ Number ∈of each recommended item>
Figure QLYQS_36
The different article serial numbers are replaced, thereby obtaining +.>
Figure QLYQS_37
Recommended first generation variant of ++>
Figure QLYQS_38
Step 5.1.4 for the first Generation pool
Figure QLYQS_39
After crossing and mutation operations are carried out on all recommended results in the first generation of all populations according to the steps 5.1.2-5.1.3, mutation recommended results of all populations in the first generation are obtained>
Figure QLYQS_40
Will->
Figure QLYQS_41
And->
Figure QLYQS_42
User group U combined into j-th group category j The method comprises the steps of (1) measuring the fitness value of each recommended result in the first-generation M new populations through a formula (4), so that environment selection is carried out on the first-generation M new populations through non-dominant sorting and crowding distances, and the optimal N recommended results are reserved as the first-generation (1) M populations;
step 5.2, information migration among the populations is carried out among different user groups:
if the mth generation of users P j,m If more than half of the recommended results are unchanged for a plurality of successive generations, computing a user group U of the j-th group category j Similarity between user groups of other group categories, and selecting the user group with highest similarity for the mth user P of the first generation j,m Performing crossover and mutation operations between all recommended results of (1) so as to obtain an mth population of the (1+1) th generation according to the process of the step 5.1.4;
step 5.3, after assigning l+1 to L, judging whether L reaches L, if not, returning to step 4.3 for sequential execution, otherwise, selecting an individual from the mth population of the L generation as the j-th group classUser group U of (2) j M th user P j,m Is a recommended result of the user.
3. The evolutionarily multitasking-based large-scale user recommendation method of claim 2, wherein said step 5.1.2 comprises:
step a, judging
Figure QLYQS_43
And->
Figure QLYQS_44
If the users belong to the same user, executing the step b, otherwise, executing the step c;
step b, pairing
Figure QLYQS_45
And->
Figure QLYQS_46
With probability c P Performing crossover operation:
randomly selecting a number r from {1,2,3 …, T }, will
Figure QLYQS_49
The former r position and->
Figure QLYQS_50
The first r bits of (a) are exchanged to obtain the first generation two cross recommended results +.>
Figure QLYQS_54
And->
Figure QLYQS_48
Figure QLYQS_51
Wherein (1)>
Figure QLYQS_53
Representation->
Figure QLYQS_55
The order of the r-th recommended item,/-)>
Figure QLYQS_47
Representation of
Figure QLYQS_52
The number of the r-th recommended article;
step c, will
Figure QLYQS_56
And->
Figure QLYQS_57
Combining to obtain a first-generation new recommended result, using formula (1) as fitness value, using binary competitive competition method to respectively select serial numbers corresponding to T items from the first-generation new recommended result and respectively forming two first-generation cross recommended results->
Figure QLYQS_58
To->
Figure QLYQS_59
As->
Figure QLYQS_60
Cross-recommendation results generated by +.>
Figure QLYQS_61
As->
Figure QLYQS_62
The cross recommendation results are generated.
4. An electronic device comprising a memory and a processor, wherein the memory is for storing a program supporting the processor to perform the large-scale user recommendation method of any one of claims 1-3, the processor being configured to execute the program stored in the memory.
5. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor performs the steps of the large-scale user recommendation method according to any of claims 1-3.
CN202310167737.1A 2023-02-27 2023-02-27 Large-scale user recommendation method based on evolutionary multitasking Pending CN116127201A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310167737.1A CN116127201A (en) 2023-02-27 2023-02-27 Large-scale user recommendation method based on evolutionary multitasking

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310167737.1A CN116127201A (en) 2023-02-27 2023-02-27 Large-scale user recommendation method based on evolutionary multitasking

Publications (1)

Publication Number Publication Date
CN116127201A true CN116127201A (en) 2023-05-16

Family

ID=86310037

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310167737.1A Pending CN116127201A (en) 2023-02-27 2023-02-27 Large-scale user recommendation method based on evolutionary multitasking

Country Status (1)

Country Link
CN (1) CN116127201A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116596640A (en) * 2023-07-19 2023-08-15 国网山东省电力公司营销服务中心(计量中心) Recommendation method, system, equipment and storage medium for electric power retail electric charge package

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116596640A (en) * 2023-07-19 2023-08-15 国网山东省电力公司营销服务中心(计量中心) Recommendation method, system, equipment and storage medium for electric power retail electric charge package

Similar Documents

Publication Publication Date Title
CN110097185B (en) Optimization model method based on generation of countermeasure network and application
Zhou et al. A survey on evolutionary construction of deep neural networks
US20190180187A1 (en) Evolving Recurrent Networks Using Genetic Programming
CN112395487B (en) Information recommendation method and device, computer readable storage medium and electronic equipment
CN116127201A (en) Large-scale user recommendation method based on evolutionary multitasking
Bai et al. A joint multiobjective optimization of feature selection and classifier design for high-dimensional data classification
CN112836794B (en) Method, device, equipment and storage medium for determining image neural architecture
CN116340726A (en) Energy economy big data cleaning method, system, equipment and storage medium
CN111325284A (en) Self-adaptive learning method and device based on multi-target dynamic distribution
Hussein et al. Deep learning and machine learning via a genetic algorithm to classify breast cancer DNA data
Mo et al. Applications of machine learning in phylogenetics
Dutta et al. Clustering by multi objective genetic algorithm
CN116611504A (en) Neural architecture searching method based on evolution
Jia et al. Weak-restriction bi-objective optimization algorithm for scheduling with rejection on non-identical batch processing machines
Sanchez Reconstructing our past˸ deep learning for population genetics
KR20230170680A (en) Multi-channel protein voxelization to predict variant pathogenicity using deep convolutional neural networks
CN110162704B (en) Multi-scale key user extraction method based on multi-factor genetic algorithm
Sharma et al. Prediction of cardiovascular diseases using genetic algorithm and deep learning techniques
CN114443986A (en) Sorting method and device, sorting model training method and device, and electronic equipment
CN113704570A (en) Large-scale complex network community detection method based on self-supervision learning type evolution
WO2023044927A1 (en) Rna-protein interaction prediction method and apparatus, and medium and electronic device
Hamazaki et al. AI-assisted selection of mating pairs through simulation-based optimized progeny allocation strategies in plant breeding
CN117976047B (en) Key protein prediction method based on deep learning
Zelený et al. Multi-Branch Multi Layer Perceptron: A Solution for Precise Regression using Machine Learning
CN114220127B (en) Image recognition method based on gradient guided evolutionary algorithm

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination